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you will do Driving innovative AI research through the development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine
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response, from which the fundamental operating wavelength can be deduced. Algorithmic extraction of those features is common practice, but very often suffers from hitting edge cases where manual parameter
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of research and innovation! Be part of change Software development of the DSP toolbox for LiFi system Implementation of algorithms and software development using C++, Python or MATLAB Technical documentation
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methods, Machine Learning algorithms, and prototypical Energy Management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a
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. Because of the specific structure of the inference problems occurring in metabolic models, direct application of these MCMC algorithms is, however, not possible. In this project, you will bring MCMC methods
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following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs
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efficient algorithms for large-scale stochastic systems Integrating data-driven methods for model estimation, learning, and validation Collaborating with interdisciplinary partners in healthcare and data
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advanced mathematical algorithms and AI frameworks for modeling and optimizing power electronic components and systems. To support our team, we are looking for a working student (all genders) in the field
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theory, ideally with a focus on mathematical approaches to quantum information theory, quantum algorithms or quantum complexity theory, or alternatively on other aspects of quantum information in
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-critial vibrations from sensor data, enabling data-driven optimization of machining processes. You will also implemet ML algorithms for additional use cases, acceleration workflows and enhancing